Cell Ranger
Bioinformatics Resource Center - Rockefeller University
http://rockefelleruniversity.github.io/Intro_To_R_1Day/
brc@rockefeller.edu
Cell Ranger
Overview
??We often put an overview slide that links to the various parts of the course content to make it easy to navigate i.e. this one for intro to r??
Set Up
Materials
??We try to denote each major section of content with these title slides (see above for set up). The hierarchy will help with the different versions of the teaching content i.e. slides and single page.??
?? Materials just gives the links to download the package Just ensure links are up to date??
All prerequisites, links to material and slides for this course can be found on github.
Or can be downloaded as a zip archive from here.
Course materials
?? this slide just shows where thye can get the course content from the download. You do not need to populate these directories. It will be made during compilation??
Once the zip file in unarchived. All presentations as HTML slides and pages, their R code and HTML practical sheets will be available in the directories underneath.
- r_course/presentations/slides/ Presentations as an HTML slide show.
- r_course/presentations/singlepage/ Presentations as an HTML single page.
- r_course/presentations/r_code/ R code in presentations.
- r_course/exercises/ Practicals as HTML pages.
- r_course/answers/ Practicals with answers as HTML pages and R code solutions.
Cell Ranger
A more advanced scRNAseq workflow
overview
A more advanced scRNAseq workflow
Full image here of workflow is here: overview
scRNA seq is a very variable process and each dataset has unique QC problems. Though our simplified approach often works, we also need many more tools in our toolbox to handle more complex datasets. Often it is a case of trial and error to see what approaches work best for your data.
Cell Ranger
Cell Ranger
Cell Ranger is a suite of tools for single cell processing and analysis available from 10X Genomics.
In this session we will demonstrate the use of Cell Ranger Count tool to process our generated fastQ and create the required files. ]
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Cell Ranger Download
Cell Ranger is available from the 10x genomics website.
Also available are pre-baked references for Human and Mouse genomes (GRCh37/38 and GRCm37)
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Cell Ranger
- Cell Ranger only runs on linux machines (CentOS/RedHat 7.0+ and Ubuntu 14.04+).
- Typically, due to memory requirements, users run Cell Ranger on a remote server and not their own machines.
- To download Cell Ranger and the required reference on remote server, we typically use the wget command [This will all be in temrinal on the server you are using.
Download the software
wget -O cellranger-7.2.0.tar.gz "https://cf.10xgenomics.com/releases/cell-exp/cellranger-7.2.0.tar.gz?Expires=1701688001&Key-Pair-Id=APKAI7S6A5RYOXBWRPDA&Signature=Puwpsqsf~wMQz5e~PwTvM2DRQO1XdJ~9zeLCWqX6tVbOx~dnf24hP1bwlmNhybr3SZUQ8C12ywcICMH6Au02wxiCRm1uuTxZ0Uvq8g~s8L8s6XFyhepdi6Qjq8dzXNGoxswg3hModjKWVptTWq-MTHBDZv~yTFB7QAM9lzHHXo6SPWg8Fnx30ngmtGC5tDReVOiJ3DY0hsFvZtG3HaQ-HEbnzEH3lre-0rpWMBlsQu-vZ4RnKE0o3Xv6pQsb6261M19nHcpCsGhDCkFjDDbradx~SNw5rpY-HMxM4SnRuaOOI0rYyDNn7xdTat3eFj7rlgATXRaYx7SYNqDYKSrNWw__"Download reference for Human genome (GRCh38)
wget -O https://cf.10xgenomics.com/supp/cell-exp/refdata-gex-GRCh38-2020-A.tar.gzCell Ranger set-up
- Having downloaded the software and references, we can then unpack them.
Unpack software and references.
tar -xzvf cellranger-7.2.0.tar.gz
tar -xzvf refdata-gex-GRCh38-2020-A.tar.gz- Finally we can add the cellranger directory to our PATH.
export PATH=/PATH_TO_CELLRANGER_DIRECTORY/cellranger-7.1.0:$PATHRunning Cell Ranger count
Now we have the downloaded Cell Ranger software and required pre-build reference for Human (GRCh38) we can start the generation of count data from scRNA-seq/snRNA-seq fastQ data.
Typically FastQ files for your scRNA run will have been generated using the Cell Ranger mkfastq toolset to produce a directory a FastQ files.
We can now use CellRanger count command with our reference and fastQ files to generate our count matrix and associated files.
If you are analyzing single nuclei RNA-seq data remember to set the –include-introns flag.
cellranger count --id=my_run_name \
--fastqs=PATH_TO_FASTQ_DIRECTORY \
--transcriptome=/PATH_TO_CELLRANGER_DIRECTORY/refdata-gex-GRCh38-2020-AWorking with custom genomes
If you are working with a genome which is not Human and/or mouse you will need to find another source for your Cell Ranger reference.
- Luckily many references are pre-built by other consortiums.
- We can build our own references using other tools in Cell Ranger.
Creating your own reference
To create your own references you will need two additional files.
- FASTA file - File containing the full genome sequence for the reference of interest
- GTF file - File containing the gene/transcript models for the reference of interest.
FASTA file (Genome sequence)
- The reference genome stored as a collection of contigs/chromosomes.
- A contig is a stretch of DNA sequence encoded as A,G,C,T,N.
- Typically comes in FASTA format.
- “>” line contains information on contig
- Lines following contain contig sequence
GTF (Gene Transfer Format)
Used to genome annotation.
Stores position, feature (exon) and meta-feature (transcript/gene) information.
Importantly for Cell Ranger Count, only features labelled as exon (column 3) will be considered for counting signal in genes
Many genomes label mitochondrial genes with CDS and not exon so these must be updated
Using Cell Ranger mkgtf
Now we have the gene models in the GTF format we can use the Cell Ranger mkgtf tools to validate our GTF and remove any unwanted annotation types using the attribute flag.
Below is an example of how 10x generated the GTF for the Human reference.
cellranger mkgtf Homo_sapiens.GRCh38.ensembl.gtf \
Homo_sapiens.GRCh38.ensembl.filtered.gtf \
--attribute=gene_biotype:protein_coding \
--attribute=gene_biotype:lncRNA \
--attribute=gene_biotype:antisense \
--attribute=gene_biotype:IG_LV_gene \
--attribute=gene_biotype:IG_V_gene \
--attribute=gene_biotype:IG_V_pseudogene \
--attribute=gene_biotype:IG_D_gene \
--attribute=gene_biotype:IG_J_gene \
--attribute=gene_biotype:IG_J_pseudogene \
--attribute=gene_biotype:IG_C_gene \
--attribute=gene_biotype:IG_C_pseudogene \
--attribute=gene_biotype:TR_V_gene \
--attribute=gene_biotype:TR_V_pseudogene \
--attribute=gene_biotype:TR_D_gene \
--attribute=gene_biotype:TR_J_gene \
--attribute=gene_biotype:TR_J_pseudogene \
--attribute=gene_biotype:TR_C_geneUsing Cell Ranger mkref
Following filtering of your GTF to the required biotypes, we can use the Cell Ranger mkref tool to finally create our custom reference.
cellranger mkref --genome=custom_reference \
--fasta=custom_reference.fa \
--genes=custom_reference_filtered.gtfCell Ranger - Output files
Cell Ranger - Outputs
Having completed the Cell Ranger count step, the user will have created a folder named as set by the –id flag for count command.
Within this folder will be the outs/ directory containing all the outputs generated from Cell Ranger count.
Cell Ranger - Count Matrices
The count matrices to be used for further analysis are stored in both MEX and HDF5 formats within the output directories.
The filtered matrix only contains detected, cell-associated barcodes whereas the raw contains all barcodes (background and cell-associated).
MEX format - filtered_feature_bc_matrix - raw_feature_bc_matrix
HDF5 format - filtered_feature_bc_matrix.h5 - raw_feature_bc_matrix.h5
Cell Ranger - BAM files
The outs directory also contains a BAM file of alignments for all barcodes against the reference (possorted_genome_bam.bam) as well as an associated BAI index file (possorted_genome_bam.bam.bai).
This BAM file is sometimes used in downstream analysis such as scSplit/Velocyto as well as for the generation of signal graphs such as bigWigs. ]
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Cell Ranger - Cloupe files
Cell Ranger also outputs files for visualisation within its own cloupe browser - cloupe.cloupe.
This allows for the visualisation of scRNA-seq/snRNA-seq as a t-sne/umap with the ability to overlay metrics of QC and gene expression onto the cells in real time
Cell Ranger - Metrics and Web Summary
Cell Ranger will also output summaries of useful metrics as a text file (metrics_summary.csv) and as a intuitive web-page.
Metrics include
- Counts/UMIs per cell.
- Number of cells detected.
- Alignment quality.
- Distribution of reads in genomic features.
- Sequencing saturation
- t-sne/UMAP with default clustering.
QC is essential
There are many potential issues which can arise in scRNA-seq/snRNA-seq data including -
- Empty droplets.
- Low quality cell (dead or dying)
- Ambient RNA contamination.
- Doublet detection ]
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Assessment of the overall quality of a scRNA-seq/snRNA-seq experiment after Cell Ranger can give our first insght into any issues we might face.
Cell Ranger - Web Summary QC
Web Summary overview
The web summary html file contains an interactive report describing the most essential QC for your single cell experiment as well as initial clustering and dimension reduction for your data.
The web summary also contains useful information on the input files and the versions used in this analysis for later reproducibility. ]
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Sample panel
The first thing we can review is the Sample information panel.
- Sample ID - Sample name (Assigned in cellranger count)
- Chemistry - The 10x chemistry used
- Include introns - Whether counting was run to include intron counts (typical for single neuron RNA-seq).
- Reference Path and Transcriptome - References used in analysis
- Pipeline Version - Version of Cell Ranger used.
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Sequencing panel
The Sequencing panel highlights information on the quality of the illumina sequencing.
- Number of reads - Total number of paired reads in library
- Valid barcodes - Number of barcodes matching barcodes in whitelist (known to be kit ~ 1 million).
- Valid UMIs - Total number of UMIs that are not all one base and contain no unknown bases.
- Sequencing saturation - Unique valid barcode/UMI versus all valid barcode/UMI
- Q30 scores - Assessment of sequencing qualities for barcode/umi/index/RNA reads.
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Sequencing panel
Key Metrics:
* Q30 Bases in RNA read > 65% (usually > 80%)
+ Reflect to Sequencing quality
+ Need to check with sequencing service supplier
* Sequencing saturation > 40% (usually range 20% ~ 80%)
+ Reflects the complexity of libraries
+ shall consider, but not necessarily, re-construct library while it too low Mapping panel
The Mapping panel highlights information on the mapping of reads to the reference genome and transcriptome.
- Reads mapped to genome - Total number of reads mapping to the genome
- Reads mapped confidently to genome - Reads mapping uniquely to the genome
- Reads mapped confidently to exonic/Intronic - Reads mapping uniquely to the exons or introns
- Reads mapped confidently to transcriptome - Reads mapping to a unique gene (and consistent with slice junctions).
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Mapping panel
Key Metrics:
* Mapped to Genome > 60% (usually range 50% ~ 90%)
+ Mapping rate to reference genome
+ Check reference genome version if too low
* Reads Mapped Confidently to Transcriptome > 30% (usually > 60%)
+ Reflection of annotation to transcriptome
+ Check annotation if too lowCells panel
The Cells panel highlights some of the most important information in the report, the total number of cells captured and the distribution of counts across cells and genes.
- Estimated number of cells - Total number of barcodes associated to at least one cell.
- Fraction reads in cells - Fraction of reads from valid barcode, associated to a cell and mapped to transcriptome.
- Median reads per cell - Median number of transcriptome reads within cell associated barcodes
- Median genes per cell - - Median number of genes detected (at least 1 count) per cell associated barcodes.
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Cells panel
Key Metrics:
- Fraction Reads in Cells > 70% (usually > 85%): + Reflects the
ambient RNA contamination + Consider correcting for ambient RNA if <
90%
- Median reads per cell > 20,000/cell and estimated number of cells
500 ~ 10,000
- May be caused by the failure of cell identification if the values were not in normal range
- Need to check knee plot and re-evaluate cell number
- Median reads per cell > 20,000/cell and estimated number of cells
500 ~ 10,000
Knee plot
The Cell panel also includes and interactive knee plot.
The knee plot shows:-
On the x-axis, the barcodes ordered by the most frequent on the left to the least frequent on the right
On the y-axis, the frequency of each ordered barcode.
Highlighted in blue are the barcodes marked as associated to cells.
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Knee plot
It is apparent that barcodes labelled blue (cell-associated barcodes) do not have a cut-off based on UMI count.
In the latest version of Cell Ranger a two step process is used to define cell-associated barcodes based on the EmptyDrops method (Lun et al.,2019).
- First high RNA containing cells are identified based on a UMI cut-off.
- Second, low UMI containing cells are used as a background training set to identify additonal cell-associated barcodes not called in first step.
If required, a –force-cells flag can be used with cellranger count to identify a set number of cell-associated barcodes.
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Knee plot
The Knee plot also acts a good QC tools to investigate differing types of single cell failure.
Whereas our previous knee plot represented a good sample, differing knee plot patterns can be indicative of specific problems with the single cell protocol.
In this example we see no specific cliff and knee suggesting a failure in the integration of oil, beads and samples (wetting failure) or a compromised sample.
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Knee plot
If there is a clog in the machine we may see a knee plot where the overall number of samples is low.
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Knee plot
There may be occasions where we see two sets of cliff-and-knees in our knee plot.
This could be indicative of a heterogenous sample where we have two populations of cells with differing overall RNA levels.
Knee plots should be interpreted in the context of the biology under investigation.
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Analysis page
The web-summary also contains an analysis page where default dimension reduction, clustering and differential expressions between clusters has been performed.
Additionally the analysis page contains information on sequencing saturation and gene per cell vs reads per cell.
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t-sne and clustering
The t-sne plot shows the distribution and similarity within your data.
- Review for high and low UMI cells driving t-sne structure and/or clustering.
- Expected separation between and structure across clusters may be observed within the t-sne plot.
- Identify expected clusters based on expression of marker genes
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Sequence and Gene saturation
The sequence saturation and Median genes per cell plots show these calculations (as show on summary page) over successive downsampling of the data.
By reviewing the curve of the down sampled metrics we can assess whether we are approaching saturation for either of these metrics.
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How this manifests going forward?
Could show examples of high MT specifics of non-specific clustering or MT clustering.
Next steps
Maybe lucky there’s no QC issues. Often at this step we wouldn tmake any desicions. But important firstt step in setting expectations/ preparing for what you may need to do for the dataset. QC and custom reads ins. Dealing with issues.